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Application of Artificial Intelligence in Capsule Endoscopy: Where Are We Now?
| Content Provider | Semantic Scholar |
|---|---|
| Author | Hwang, Youngbae Park, Junseok Lim, Yun Jeong Chun, Hoon Jai |
| Copyright Year | 2018 |
| Abstract | Unlike wired endoscopy, capsule endoscopy requires additional time for a clinical specialist to review the operation and examine the lesions. To reduce the tedious review time and increase the accuracy of medical examinations, various approaches have been reported based on artificial intelligence for computer-aided diagnosis. Recently, deep learning-based approaches have been applied to many possible areas, showing greatly improved performance, especially for image-based recognition and classification. By reviewing recent deep learning-based approaches for clinical applications, we present the current status and future direction of artificial intelligence for capsule endoscopy. |
| Starting Page | 547 |
| Ending Page | 551 |
| Page Count | 5 |
| File Format | PDF HTM / HTML |
| DOI | 10.5946/ce.2018.173 |
| PubMed reference number | 30508880 |
| Journal | Medline |
| Volume Number | 51 |
| Journal | Clinical endoscopy |
| Alternate Webpage(s) | http://www.e-ce.org/upload/pdf/ce-2018-173.pdf |
| Alternate Webpage(s) | https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/94/d1/ce-2018-173.PMC6283750.pdf |
| Alternate Webpage(s) | https://doi.org/10.5946/ce.2018.173 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
